Towards Optimal Software Adoption and Distribution

Since the very beginning of software industry, it’s always been the same: applying the most innovative ways towards lowering the friction costs of software adoption is the key to success, especially in winner-takes-all market and platform-plays.

From the no-cost software bundled with the old mainframes to the freeware of the ‘80s and the free-entry web applications of the 90’s, the pattern is clear: good’n’old pamphlet-like distribution to spread software as it were the most contagious of ideas.

It comes to the realization that the cost of learning to use some software is much higher than the cost of software licenses; or that it’s complementary to some more valuable work skills; or that the expected future value from owning the network created by its users would be higher that selling the software itself. Never mind, until recently, little care has been given to reasoning from first principles about the tactics and strategies of software distribution for optimal adoption, so the only available information are practitioner’s anecdotes with no verifiable statistics, let alone a corpus of testable predictions. So, it’s refreshing to find and read about these matters from a formalized perspective:

The most remarkable result of the paper is that, in the case of a very realistic scenario of random spreading of software with limited control and visibility over who gets the demo version, an optimal strategy is offered with conditions under which the optimal price is not affected by the randomness of seeding: just being able to identify and distribute to the low-end half of the market is enough for optimal price formation, since its determination will depend on the number of distributed copies and not on the seeding outcome. But with multiple pricing and full control of the distribution process (think registration-required freemium web applications) the optimal strategy is to charge non-zero prices to the higher half-end of the market, in deep contrast with the single-digits percentage of the paying customers in real world applications, which suggest that too much money is being left on the table.